manager.assign_sets(train=train) tup = manager.create_mask( train.iloc[:, :-1], global_dirs.variable_selection[0], select=global_dirs.variable_selection[1] ) # This tuple shouldn't take care about y_column index scalers = manager.preprocess_train(tup, scale_Y=False) dnn_model = manager.fit_dnn_regression([(9, 'relu'), (18, 'relu'), (56, 'relu'), (11, 'relu'), (10, 'relu')], epochs=300, batch_size=30, use_dropout=False) results = manager.predict_dnn_regression(test, tup) #X_train = train.loc[:, train.columns != train.columns[-1]] #X_test = test.loc[:, test.columns != test.columns[-1]] #var_selection = reader.create_mask(X_train, global_dirs.variable_selection[0], select=global_dirs.variable_selection[1]) #X_train = X_train.loc[:, var_selection] #X_test = X_test.loc[:, var_selection] #y_train = train.loc[:, train.columns[-1]] #y_test = test.loc[:, test.columns[-1]] #X_train = scaler_X.fit_transform(X_train) #X_test = scaler_X.transform(X_test)
"oxygen": "green", "iron": "orange", } dec_round = 3 plt.rc('axes', labelsize=25) plt.rc('axes', titlesize=25) plt.rc('legend', fontsize=15) plt.rc('xtick', labelsize=15) plt.rc('ytick', labelsize=15) results = None for name, ds in test.items(): results = manager.predict_dnn_regression(ds, tup) f, ax = plt.subplots(1, 2) # Histogram of differences ax[0].hist(results["differences"], color=colors[name], histtype="step", lw=2) ax[0].axvline(x=0) ax[0].set_title( "Deep Neural Net\nQGSJET-II (test)\nHistogram of differences - {}". format(name)) ax[0].set_xlabel(r'$S_{\mu}^{real} - S_{\mu}^{pred}$') ax[0].set_ylabel("Count") dmean_patch = mpatches.Patch(color='white', label='Diff. mean: {}'.format(